F

Fireworks AI

AI inference platform for model deployment

AI Deployment Strategist

Implementation & Technical Account ManagementConfirmed live in the last 24 hours
Full-time$165k - $220k /yrHybrid

About the Job

Requirements

  • A bachelor's degree is required.
  • At least 4 years of experience in a technical, customer-facing role such as Deployment Strategy, Technical Account Management, Solutions Engineering, Forward-Deployed Engineering, or Technical Customer Success at an enterprise software or AI/ML company.
  • A strong technical foundation, including familiarity with APIs and cloud infrastructure such as AWS, GCP, or Azure, and hands-on coding ability sufficient to debug integrations.
  • Direct experience with large language models in production, including understanding probabilistic model behavior, prompt engineering, fine-tuning, retrieval-augmented generation, and/or agent architectures.
  • A track record of managing enterprise relationships end-to-end, including technical delivery, executive communication, and commercial outcomes such as renewals and expansion.
  • Ability to translate fluently between machine learning engineers and business stakeholders in written and verbal communication.
  • Comfort owning ambiguity in a fast-moving, early-stage function and shaping the role's playbook.

Responsibilities

  • Partner with Sales during late-stage deals and early post-sale to map customers' technical and organizational landscape, stakeholders, success criteria, and risks.
  • Quantify deployment business cases, including cost-to-serve, latency and quality targets, and return on investment relative to customers' current approaches; define scoped pilots with milestones and exit criteria.
  • Author internal scoping briefs for AI Field Engineering, owning the rationale and scope while AI Field Engineering owns implementation details.
  • Own post-sale technical relationships from kickoff through go-live, including model deployment, integration architecture, single sign-on and security configuration, and performance benchmarking.
  • Partner with AI Field Engineering to transition deployments successfully from proof of concept to production.
  • Build and execute joint success plans with clear milestones, ownership, and timelines.
  • Serve as the primary technical point of contact for strategic accounts and build relationships with engineering leaders, machine learning and platform teams, and power users.
  • Advise customers on model selection, fine-tuning, prompt engineering, latency and cost optimization, and agent architecture as their usage matures.
  • Drive usage against business objectives and measurable outcomes, including latency service-level agreements, cost per token, model quality, and uptime.
  • Serve as the technical escalation point for production issues and coordinate with Engineering and Support to resolve them.
  • Maintain runbooks and playbooks that reduce time to resolution across the account portfolio.
  • Synthesize patterns across accounts and feed them into product and engineering roadmaps.
  • Represent customer priorities in internal planning, particularly for model support, tooling gaps, and platform reliability.
  • Track feedback between customers' development needs and product releases.
  • Own the technical narrative for quarterly and executive business reviews, including adoption trends, return on investment, and roadmap alignment.
  • Partner with Account Executives on renewal strategy and identify expansion opportunities tied to new use cases, teams, or workloads.
  • Forecast account health and proactively flag risks to retention.

Desired Qualifications

  • Experience scoping high-value engagements, including value-based or total-cost-of-ownership and return-on-investment framing, defining pilot success criteria, and writing scoping documents or product requirements documents, gained in a Deployment Strategist, Forward-Deployed Engineer, or similar field product-management capacity.
  • Experience with inference optimization, model serving infrastructure, or open-source model ecosystems such as Llama, Mixtral, or DeepSeek.

About the Company

Fireworks AI

AI inference platform for model deployment

Fireworks AI provides an AI inference platform that helps organizations run, customize, and deploy machine learning models. It supports deployment, fine-tuning, and inference workflows, with access to open-source models and on-demand deployment options through a subscription model. The platform enables users to configure and optimize models for production environments, manage variants, and scale AI workloads while controlling costs. Unlike some competitors, the emphasis is on end-to-end production-grade inference and fine-tuning across a range of clients, from tech firms to research institutions and enterprises, with tools tailored for rapid deployment and cost efficiency. The long-term goal is to expand the use of AI in production by building compound AI systems, growing the team, and delivering broader platform capabilities that accelerate AI adoption in real-world settings.

Company size
201-500
Company stage
Series D
Total funding
$1.8B
Headquarters
Redwood City, CA
Founded
2022

Recently Posted Jobs

Explore more opportunities at Fireworks AI

See more jobs